EP115 Bibliometric analysis of research on the anesthesia in hip fracture over the last decade
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant Background and Aims A bibliometric approach using network analytical methods was applied to explore the research trends on anesthesia for hip fractures. Methods Publications related to anesthesia for hip fractures from 2013 to 2023 were retrieved from the Web of Science. The keywords were ‘Anesthesia and hip fracture’, ‘Anesthesia in hip fracture’, ‘Fascia Iliaca Block’, Fascia Iliaca Compartment Block’, and ‘Pericapsular Nerve Group Block’. The extracted records were analyzed in terms of publication year, research area, journal title, country, organization, authors, and keywords. The research trends on anesthesia for hip fractures were visualized using the VOSviewer program. Results Analyses of 1022 articles revealed that total number of publications has continually increased over the last decade (figure 1). The country producing the most articles was the US, followed by China, Turkey, England, Canada, and India (table 1). It was seen that most articles were published in Medicine, Cureus Journal of Medical Science, Journal of Orthopaedıc Trauma, Regional Anesthesia and Pain Medicine (table 1). A network analysis based on the cooccurrence of keywords revealed the following two major study designs: clinical study and research methodology. It was determined that there was an increase in the number of studies on anesthesia in hip fractures (figure 2). The most used keywords were hip fracture, pain, anesthesia, analgesia, nerve bloc, fascia iliaca compartment bloc, pericapsular nerve group bloc, fascia iliaca bloc, and femoral nerve bloc. Conclusions This study examined the research trends on anesthesia in hip fractures using bibliometric methods. Findings provide useful guidelines for researchers in searching for relevant topics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.126 | 0.203 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".